Mediating and Moderating Effects in Ageism and Depression among the Korean Elderly: The Roles of Emotional Reactions and Coping Reponses
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the relationship between ageism and depression, exploring the stress-mediating and stress-moderating roles of emotional reactions and coping behaviors. METHODS: Data were from the 2013 Ageism and Health Study (n = 816), a cross-sectional survey of urban and rural community-dwelling seniors aged 60-89 years in South Korea. Participants with at least one experience of ageism reported on their emotional reactions and coping responses. The measure yielded two types of coping: problem-focused (taking formal action, confrontation, seeking social support) and emotion-focused (passive acceptance, emotional discharge). RESULTS: Although ageism was significantly associated with depressive symptoms (B = 0.27, p < 0.0001), the association was entirely mediated by emotional reactions such as anger, sadness, and powerlessness. Problem-focused coping, especially confrontation and social support, seemingly reduced the impact of emotional reactions on depression, whereas emotion-focused coping exacerbated the adverse effects. CONCLUSION: These findings support the cultural characterization explanation of ageism and related coping processes among Korean elderly and suggest that regulating emotional reactions may determine the efficacy of coping with ageism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".